Most recently, similar to our intuition, some researchers attempt to revisit the role of IoU for object detection. First, methods such as Cascade R-CNN [1]} and ALFNet [2]} try to replace the single threshold with an incremental IoU setting. Although these detectors have provided solutions that gradually refine the proposals, they are still not free from the hand-crafted setting and perform somewhat inflexible during training. Most recently, a progressive network [3]} is proposed with three-phase progression to gradually refine anchors following human annotation process. However, the problem is also obvious. On the one hand, although the gradual strategy could bring performance improvements, it also brings double the computational cost. On the other hand, some incremental settings are not flexible. For example, Cascade R-CNN and ALFNet both use manually defined IoU (e.g, 0.5, 0.6 and 0.7) to measure the quality.
